Astrodynamics最新文献

Spacecraft trajectory optimization is essential for all the different phases of a space mission, from its launch to end-of-life disposal. Due to the increase in the number of satellites and future space missions beyond our planet, increasing the level of autonomy of spacecraft is a key technical challenge. In this context, traditional trajectory optimization methods, like direct and indirect methods are not suited for autonomous or on-board operations due to the lack of guaranteed convergence or the high demand for computational power. Heuristic control laws represent an alternative in terms of computational power and convergence but they usually result in sub-optimal solutions. Successive convex programming (SCVX) enables to extend the application of convex optimization to non-linear optimal control problems. The definition of a good value of the trust region size plays a key role in the convergence of SCVX algorithms, and there is no systematic procedure to define it. This work presents an improved trust region based on the information given by the nonlinearities of the constraints which is unique for each optimization variable. In addition, differential algebra is adopted to automatize the transcription process required for SCVX algorithms. This new technique is first tested on a simple 2D problem as a benchmark of its performance and then applied to solve complex astrodynamics problems while providing a comparison with indirect, direct, and standard SCVX solutions.

Heteroclinic connections represent unique opportunities for spacecraft to transfer between isoenergetic libration point orbits for zero deterministic ΔV expenditure. However, methods of detecting them can be limited, typically relying on human-in-the-loop or computationally intensive processes. In this paper we present a rapid and fully systematic method of detecting heteroclinic connections between quasi-periodic invariant tori by exploiting topological invariants found in knot theory. The approach is applied to the Earth–Moon, Sun–Earth, and Jupiter–Ganymede circular restricted three-body problems to demonstrate the robustness of this method in detecting heteroclinic connections between various quasi-periodic orbit families in restricted astrodynamical problems.

Spaceborne global navigation satellite system (GNSS) has significantly revolutionized the development of autonomous orbit determination techniques for low Earth orbit satellites for decades. Using a state-of-the-art combination of GNSS observations and satellite dynamics, the absolute orbit determination for a single satellite reached a precision of 1 cm. Relative orbit determination (i.e., precise baseline determination) for the dual satellites reached a precision of 1 mm. This paper reviews the recent advancements in GNSS products, observation processing, satellite gravitational and non-gravitational force modeling, and precise orbit determination methods. These key aspects have increased the precision of the orbit determination to fulfill the requirements of various scientific objectives. Finally, recommendations are made to further investigate multi-GNSS combinations, satellite high-fidelity geometric models, geometric offset calibration, and comprehensive orbit determination strategies for satellite constellations.

CubeSats have become versatile platforms for various space missions (e.g., on-orbit servicing and debris removal) owing to their low cost and flexibility. Many space tasks involve proximity operations that require precise guidance, navigation, and control (GNC) algorithms. Vision-based navigation is attracting interest for such operations. However, extreme lighting conditions in space challenge optical techniques. The on-ground validation of such navigation systems for orbital GNC becomes crucial to ensure their reliability during space operations. These systems undergo rigorous testing within their anticipated operational parameters, including the exploration of potential edge cases. The ability of GNC algorithms to function effectively under extreme space conditions that exceed anticipated scenarios is crucial, particularly in space missions where the scope of errors is negligible. This paper presents the ground validation of a GNC algorithm designed for autonomous satellite rendezvous by leveraging hardware-in-the-loop experiments. This study focuses on two key areas. First, the rationale underlying the augmentation of the robot workspace (six-degree-of-freedom UR10e robot + linear rail) is investigated to emulate relatively longer trajectories with complete position and orientation states. Second, the control algorithm is assessed in response to uncertain pose observations from a vision-based navigation system. The results indicate increased control costs with uncertain navigation and exemplify the importance of on-ground testing for system validation before launch, particularly in extreme cases that are typically difficult to assess using software-based testing.

While novel artificial intelligence and machine learning techniques are evolving and disrupting established terrestrial technologies at an unprecedented speed, their adaptation onboard satellites is seemingly lagging. A major hindrance in this regard is the need for high-quality annotated data for training such systems, which makes the development process of machine learning solutions costly, time-consuming, and inefficient. This paper presents “the OPS-SAT case”, a novel data-centric competition that seeks to address these challenges. The powerful computational capabilities of the European Space Agency’s OPS-SAT satellite are utilized to showcase the design of machine learning systems for space by using only the small amount of available labeled data, relying on the widely adopted and freely available open-source software. The generation of a suitable dataset, design and evaluation of a public data-centric competition, and results of an onboard experimental campaign by using the competition winners’ machine learning model directly on OPS-SAT are detailed. The results indicate that adoption of open standards and deployment of advanced data augmentation techniques can retrieve meaningful onboard results comparatively quickly, simplifying and expediting an otherwise prolonged development period.

The limited space around the Earth is getting cluttered with leftover fragments from old missions, creating a real challenge. As more satellites are launched, even debris pieces as small as 5 mm must be tracked to avoid collisions. However, it is an arduous and challenging task in space. This paper presents a technical exploration of ground-based and in-orbit space debris tracking and orbit determination methods. It highlights the challenges faced during on-ground and in-orbit demonstrations, identifies current gaps, and proposes solutions following technological advancements, such as low-power pose estimation methods. Owing to the numerous atmospheric barriers to ground-based sensors, this study emphasizes the significance of spaceborne sensors for precise orbit determination, complemented by advanced data processing algorithms and collaborative efforts. The ultimate goal is to create a comprehensive catalog of resident space objects (RSO) around the Earth and promote space environment sustainability. By exploring different methods and finding innovative solutions, this study contributes to the protection of space for future exploration and the creation of a more transparent and precise map of orbital objects.

The tradeoff between accuracy and efficiency in gravitational field modeling for binary asteroid landing is one of the challenges in dynamical analyses. Four representative gravitational modeling methods are employed and compared in this study. These are the sphere–sphere model, ellipsoid–sphere model, inertia integral-polyhedron method, and finite element method. This study considers the differences between these four models, particularly their effects on the landing dynamics of a lander. A framework to simulate the coupled orbit–attitude motion of a lander in a binary system is first established. Numerical simulations are then performed on the natural landings on the second primary of the (66391) Moshup–Squannit system. The results show significant differences in the final landing dispersions, settling time, and sliding distance when applying the simplified models. On the basis of the modeling accuracy and computational efficiency, the finite element method should be chosen for future missions.

This study examines the impact of electric solar wind sail (E-sail) parameters on the attitude stability of E-sail’s central spacecraft by using a comprehensive rigid-flexible coupling dynamic model. In this model, the nodal position finite element method is used to model the elastic deformation of the tethers through interconnected two-node tensile elements. The attitude dynamics of the central spacecraft is described using a natural coordinate formulation. The rigid-flexible coupling between the central spacecraft and its flexible tethers is established using Lagrange multipliers. Our research reveals the significant influences of parameters such as tether numbers, tether’s electric potential, and solar wind velocity on attitude stability. Specifically, solar wind fluctuations and the distribution of electric potential on the main tethers considerably affect the attitude stability of the spacecraft. For consistent management, the angular velocities of the spacecraft must remain at target values. Moreover, the attitude stability of a spacecraft has a pronounced dependence on the geometrical configuration of the E-sail, with axisymmetric E-sails proving to be more stable.

The solar sail is one of the most promising space exploration systems due to its theoretically infinite specific impulse achieved through solar radiation pressure (SRP). Recently, researchers have proposed “transformable spacecraft” capable of actively reconfiguring their body configurations using actuatable joints. Transformable spacecraft, if used similarly to solar sails, are expected to significantly enhance orbit and attitude control capabilities owing to their high redundancy in control degrees of freedom. However, controlling them becomes challenging due to their large number of inputs, leading previous researchers to impose strong constraints to limit their potential control capabilities. This study focuses on novel attitude control techniques for transformable spacecraft under SRP. We developed two methods, namely, joint angle optimization to obtain arbitrary SRP force and torque, and momentum damping control driven by joint angle actuation. Our proposed methods are formulated in a general manner and can be applied to any transformable spacecraft comprising front faces that can predominantly receive the SRP on each body. The validity of our proposed method is confirmed through numerical simulations. Our study contributes to making most of the high control redundancy of transformable spacecraft without the need for expendable propellants, thus significantly enhancing the orbit and attitude control capabilities.

In real scenarios, the spacecraft deviates from the intended paths owing to uncertainties in dynamics, navigation, and command actuation. Accurately quantifying these uncertainties is crucial for assessing the observability, collision risks, and mission viability. This issue is further magnified for CubeSats because they have limited control authority and thus require accurate dispersion estimates to avoid rejecting viable trajectories or selecting unviable ones. Trajectory uncertainties arise from random variables (e.g., measurement errors and drag coefficients) and processes (e.g., solar radiation pressure and low-thrust acceleration). Although random variables generally present minimal computational complexity, handling stochastic processes can be challenging because of their noisy dynamics. Nonetheless, accurately modeling these processes is essential, as they significantly influence the uncertain propagation of space trajectories, and an inadequate representation can result in either underestimation or overestimation of the stochastic characteristics associated with a given trajectory. This study addresses the gap in characterizing process uncertainties, represented as Gauss–Markov processes in mission analysis, by presenting models, evaluating derived quantities, and providing results on the impact of spacecraft trajectories. This study emphasizes the importance of accurately modeling random processes to properly characterize stochastic spacecraft paths.